Evaluation of Transfer Learning for Visual Multiple Target Tracking

Deep learning has revolutionized high-level image processing tasks, notably image classification and segmentation, by effectively handling multi-dimensional features in image space. This report investigates the application of transfer learning, utilizing features extracted from a pre-trained VGG deep neural network to visual multiple-target tracking (MTT). Traditional feature detectors like the Lucas-Kanade Tracker and Good Features to Track (GFTT) algorithm are computationally efficient but limited in dynamic, cluttered environments, such as those encountered by unmanned aerial systems (UAS). Replacing GFTT with deep feature detectors from VGG is proposed to assess tracking accuracy and improve computational efficiency. Our experiments reveal that, though promising, deep feature extraction results in lower frame rates and less smooth trajectories than traditional methods. This study highlights the potential and current limitations of integrating deep learning features into real-time visual tracking systems. Plans include optimizing the deep learning model for real-time processing, automating filter selection for feature extraction, and exploring additional pre-trained networks to improve tracking performance and efficiency.